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VEO-Engine: Interfacing and Reasoning with an Emotion Ontology for Device Visual Expression.

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Machines can now understand user emotions using the Visualized Emotion Ontology (VEO) and VEO-Engine software. This enables smart devices to express emotions, improving human-computer interaction, especially in clinical settings.

Keywords:
EmotionsOntologySemantic webmHealth Affective computing

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Area of Science:

  • Computer Science
  • Human-Computer Interaction
  • Ontology Engineering

Background:

  • Machines require formal definitions of emotions for affective computing.
  • Clinical settings present unique challenges for emotion recognition due to patient stress.
  • Existing systems lack robust methods for machines to express and interpret user emotions.

Purpose of the Study:

  • To develop and test the Visualized Emotion Ontology (VEO) and its associated software engine (VEO-Engine).
  • To enable machines to formally define, visualize, and interpret user emotions.
  • To demonstrate the feasibility of using VEO and VEO-Engine in human-computer interaction.

Main Methods:

  • Developed the Visualized Emotion Ontology (VEO) linking abstract visualizations to specific emotions.
  • Created the VEO-Engine software API (Java, Apache Jena, OWL-API) to interface with VEO.
  • Tested the VEO-Engine on a Raspberry Pi with a touchscreen, linking visualizations to emotions.
  • Implemented an ontology-based reasoner for interpreting user emotions based on input parameters.

Main Results:

  • The VEO successfully links abstract visualizations to specific emotions.
  • The VEO-Engine demonstrated functionality in interpreting user emotions.
  • The system was tested on a portable device, showing feasibility for real-world applications.
  • Wireless interfacing capabilities were established for smart devices.

Conclusions:

  • The Visualized Emotion Ontology (VEO) and VEO-Engine provide a viable method for machines to understand and express emotions.
  • The developed system shows portability and usability for human-computer interaction.
  • This approach has significant potential for applications in clinical settings and beyond.